Systems and methods for rapid development of object detector models

ABSTRACT

A computer vision system configured for detection and recognition of objects in video and still imagery in a live or historical setting uses a teacher-student object detector training approach to yield a merged student model capable of detecting all of the classes of objects any of the teacher models is trained to detect. Further, training is simplified by providing an iterative training process wherein a relatively small number of images is labeled manually as initial training data, after which an iterated model cooperates with a machine-assisted labeling process and an active learning process where detector model accuracy improves with each iteration, yielding improved computational efficiency. Further, synthetic data is generated by which an object of interest can be placed in a variety of setting sufficient to permit training of models. A user interface guides the operator in the construction of a custom model capable of detecting a new object.

RELATED APPLICATIONS

This application claims the benefit of U.S. Patent Application Ser. No.63/337,595 filed May 2, 2022, and further claims the benefit of U.S.Patent Application Ser. No. 63/329,327 filed Apr. 8, 2022. It is also acontinuation-in-part of U.S. patent application Ser. No. 17/866,396filed Jul. 15, 2022, which is a 371 conversion of PCT ApplicationPCT/US21/13940 filed Jan. 19, 2021, which in turn is acontinuation-in-part of U.S. patent application Ser. No. 16/120,128filed Aug. 31, 2018, which in turn claims the benefit of U.S. PatentApplication Ser. No. 62/553,725 filed Sep. 1, 2017. Further, thisapplication is a continuation-in-part of U.S. patent application Ser.No. 17/866,389 which is a 371 conversion of PCT Application No.PCT/US21/13932, both of which in turn claim the benefit of U.S. PatentApplications Ser. No. 62/962,928 and Ser. No. 62/962,929, both filedJan. 17, 2020, and also U.S. Patent Application Ser. No. 63/072,934,filed Aug. 31, 2020. The present application claims the benefit of eachof the foregoing, all of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to computer vision systemsconfigured for detection and recognition of objects in video and stillimagery in a live or historical setting, and more particularly relatesto the development of teacher-student object detector models thatimprove computational efficiency and, in related aspects, enabletraining of a network with reduced numbers of training images throughthe use of machine assisted labeling, active learning and iterativetechniques to achieve desired levels of accuracy in object detectormodels.

BACKGROUND OF THE INVENTION

Conventional computer vision and machine learning systems are configuredto identify objects, including people, cars, trucks, etc., by developinga computer vision model trained to recognize features of the object orobjects. More generally, conventional imagery processing systems utilizeone or more learned models to detect objects of interest in still frameimages or in frames of video.

Deep learning methods and techniques have become standard for use incomputer vision applications as well as other areas of artificialintelligence. In particular, Convolutional Neural Networks (CNN) aregenerally regarded as providing state-of-the-art results. An approach tobuilding a computer vision model using CNNs can be generalized as havingfour steps, where the first step includes a key difference betweenclassifiers and detectors. In either, the first step is to create adataset comprised of annotated images, if one does not already exist.For classifiers, the objective annotations simply provide a confidencevalue that a given image includes at least one occurrence of an objectof interest, and the image receives only one label indicating the classof the object of interest regardless how may such objects occur in thatimage. Detectors, in contrast, both identify and locate each occurrenceof an object of interest in an image, where a bounding box is drawnaround each occurrence with a label for each bounding box. For example,if the object of interest in a dog and a given image includes two dogs,a classifier will label the entire image “Dog”. A detector will put abounding box around each of the two dogs, and label both boxes “Dog.”

As a second step, features pertinent to the task at hand are extractedfrom each image. This is a key point in modeling the problem. Forexample, the features used to recognize faces, features based on facialcriteria, are obviously not the same as those used to recognize touristattractions or human organs, although some models are trained to detectmany classes of objects. Then, as a third step, a deep learning model istrained based on the features isolated. Such training means feeding themachine learning model many images and it will learn, based on thosefeatures, how to solve the task at hand, i.e., detecting images thatinclude objects with those features. Training typically includes bothpositive and negative training, where negative training refers to imagesthat do not include the objects of interest. Last, the fourth step is toevaluate the model using images that weren't used in the training phase.By doing so, the accuracy of the training model can be tested.

While the foregoing strategy works for the initial development of amodel, it presents a number of challenges, especially for detectors asopposed to classifiers, where there is a desire to be able to detect anadditional class of object for which the original model was not trained.Among those challenges is the need, in most systems, to have a datasetwith a large number of images. A common rule of thumb is 1,000 imagesfor each class. A further issue for developing object detection modelsas opposed to classifiers is the time required to achieve the labelingnecessary to develop an object detection model from scratch. One of thechallenges of labeling is the confusion that can result when everyinstance of the object of interest in an image is not labeled. Therequirement for both accuracy and a sizable number of images makes itdifficult for persons who have not received specialized training todevelop an object detection model that will perform with reasonableaccuracy. It should also be understood that the images need to bediverse and representative of real-world conditions to yield goodresults.

When there is a need to build a model capable of detecting a differentobject than the existing model was designed, prior art solutions faceeven more challenges. One significant challenge with adding newer objecttypes is the increased labeling costs, while a second significantchallenge is the increased computational expense. For example, a modelM1 has been trained on dataset D1 for a set of objects O1. The customeris now interested in an additional set of objects O2, with instances ofO2 in the images of a new dataset D2. The new dataset D2 is as yetunlabeled for any objects, which results in the aforesaid risinglabeling costs and rising run time costs.

The rising labeling costs result because one cannot simply label O2objects in D2 and then train a combined (O1+O2) detector on the dataset(D1+D2). This is because D1 may have occurrences of objects from the setof objects O2 that will not be labeled since the user was initiallyinterested in only the set of objects O1. Where objects appear in animage, but are not labeled, during training the detector identifies theunlabeled objects as not being of interest, and so detection of thoseobjects is excluded from the model. The result is that training acombined (O1+O2) detector on (D1+D2) in this manner will lead to falsenegatives. To avoid this, in conventional approaches the images of D1must be revisited to label any of the set of objects O2 present in thoseimages. For similar reasons, all O1 objects would need to be labeled inthe images of dataset D2. Both labeling tasks can be very laborious, asthe list of objects, and the amount of data keeps growing over time.

An alternative approach might be to label only O2 objects in D2, andbuild a new model M2 that only detects 02 objects. In the productionenvironment, for each new image, the model M1 would first be run on theproduction images, followed by running the model M2 on the productionimages. The downside is that such a process results in additionalcomputational time per image, which increases every time the customer isinterested in a new set of objects. If the process of running anadditional model for every new set of objects is continued indefinitely,there will be a time when the total computational expense becomesprohibitive.

When the objective of the system is only classification, rather thandetection, a technique termed Transfer Learning has been used to reducecomputational expense, for example by the use of a Teacher-Studentmodel. In classifiers using such Transfer Learning techniques, analready-trained model serves as a starting point. Typically thatalready-trained model has seen a large number of images and learned todistinguish among the classes. If so, that classifier can be taught afew new classes in the same domain (i.e., generally same type ofobjects) based on a relatively small number of training images. However,the greater complexity of object detection models has made suchconventional Transfer Learning techniques unworkable for detection.

Because of the foregoing challenges, typical end-users have refrainedfrom developing their own object detection models and instead haverelied on third parties to develop such models. This limitation makes itdifficult for such a customer to develop a model where the mereknowledge that a search is being performed for the object of interest ishighly confidential.

As a result, there has been a long-felt need for a system and methodthat would provide the benefit of Transfer Learning to object detectionmodels. Further, there has been a long-felt need for a system and methodfor developing object detection models that can be executed by anend-user without significant specialized training and allows the subjectof the search to remain confidential within the end-user organization.Still further, there has been a long felt need for a schema fordeveloping an object detection model for a new object where onlycomparatively few images are available for training while still yieldingacceptable accuracy and minimizing computational expense.

SUMMARY OF THE INVENTION

The present invention substantially resolves the limitations ofconventional systems performing object detection in that it provides aprocess and system by which a user without specialized training candevelop custom object detection models using substantially fewer imagesthan in conventional systems, while permitting the developer of themodel to maintain confidentiality regarding the object of interest. Inan embodiment, the system receives from a user an image datasetcomprising a quantity of images, either in the form of still frameimages or in the form of video snippets comprising a sequence of videoframes. The volume of images is reduced compared to conventionalsystems, and at least in some embodiments forms a small dataset.

A randomly selected batch of images is selected from the image datasetand occurrences of the object of interest are labeled on the imagesincluded in the batch. Those labeled images form training data for adeep learning network, and once the network is trained a first iterationof a custom model is developed, where that model typically is specificto a much more limited number of classes, and in at least some casesjust one class. The system also includes a system production model,which in some embodiments has been extensively trained to detect avariety of classes of objects. The system production model and theiterated model operate as teacher models in an teacher-student network,where the classes that each of the teacher models are trained to detectare combined in an optimization process to yield a merged, or studentmodel that can detect all of the objects that either teacher model candetect. In an exemplary embodiment the optimization process comprises aclassifier and a regressor run at anchor boxes across the image, whichare specific locations in the image, at various aspect ratios. Themerged model is run against a production dataset which can compriseeither still frame images or frames from video sequences, which are fedback to the original images and video for correction of labeling errorsand/or updating of missed labelings. By iterating through several roundsof selecting a batch of images and/or correcting any mislabeled imagesfrom a prior batch, the merged or student model converges and yieldsusable results.

To further improve results, the iterated model is also provided to amachine assisted labeling process as well as an active learning process.The outputs of both are also fed back to the original images and videoto allow correction of mislabeled images or partly labeled images. Inaddition, the video output is supplied to a tracking process thatidentifies the location of objects in sequential frames of the video,whereby only a single frame can be labeled initially and the trackingprocess receives that labeling data and can apply it to the remainingframes of the video snippet. The output of the tracking process then iscombined with the labeled still frame images to yield training data forthe custom model. It will be appreciated by those skilled in the artthat, in implementations where the system production model does not yetexist, the development of the system production model can be efficientlyachieved by the process of developing the iterated model, in someembodiments including the machine assisted labeling and active learningsubprocesses described in greater detail hereinafter.

For instances in which an object, or at least a CAD or similar modelthereof, is available but a suitable volume of different images is not,in a related aspect of the invention a wide variety of synthetic imagescan be generated.

It is therefore one object of the present invention to provide a deeplearning system capable of combining two or more teacher models trainedfor detection of different objects into a single student model capableof detecting all of the objects of the two or more teacher models.

It is another object of the present invention to provide a deep learningsystem capable of combining two or more teacher models, trained fordetection of different objects where at least one is trained fordetection of a plurality of objects, into a single student modelconfigured to detect all of the objects of the two or more teachermodels.

A further object of the present invention is to provide a deep learningsystem capable of combining two or more teacher models, each trained fordetection of one or more objects wherein at least one of the teachermodels is trained to detect objects different from the objects the otherteacher models are trained to detect, into a single student modelconfigured to detect a combination of objects comprising one or moreobjects from each teacher model.

It is a further object of the present invention to provide a deeplearning system capable of combining two or more teacher models trainedfor classification and detection of different objects into a singlestudent model that is able to detect all the objects.

It is a further object of the invention to provide a system wherein newclasses of objects can be added to a previously developed and trainedobject detector network without requiring an operator to relabel objectsin the training data of the previously developed object detector.

It is yet another object of the present invention to provide a deeplearning system and method configured to achieve acceptable accuracyusing partially labeled datasets to train a model.

It is another object of the present invention to provide a deep learningsystem and method that achieves acceptable accuracy while usingdifferent sets where only a single or only some objects of interest arelabeled.

It is a further object of the present invention to provide a deeplearning network and process that uses active learning to reduce thenumber of labeled images required to develop a model successfully.

It is a still further object of the present invention to provide asystem and process for improving object classification and detectionaccuracy through the use of machine assisted labeling.

Yet another object of the present invention is to provide a system andprocess for improving object detection through the use of activelearning.

It is another object of the present invention to provide a system andprocess for improving object detection through the iterative applicationof low shot learning to a reduced image set.

It is an additional object of the present invention to provide a deeplearning system capable of combining two or more teacher models trainedfor classification and detection of different objects into a singlestudent model containing a combination of objects from each of theteacher models.

A still further object of the invention is to provide an optimizationprocess for teacher-student models comprising distillation.

Another object of the present invention is to provide an optimizationprocess for teacher-student models comprising a classifier and aregressor,

Yet a further object of the present invention is to provide a system andmethod having improved computational efficiency for optimizing a mergedmodel.

It is yet another object of the present invention to provide a systemand method for developing a system production model through use of aniterative model augmented with active learning, machine labeling, orboth.

A further object of the present invention is to provide a system andmethod for ordering data such as images according to an uncertaintyscore.

Yet a further objection of the present invention is to provide a systemand method for visually differentiating labels proposed by the systemfor operator review from labels below a threshold.

A still further object of the present invention is to provide a systemand method for providing to an operator an opportunity to review imageshaving an uncertainty score above a threshold value, where the operatorcan be either automated or human.

These and other objects of the invention can be better appreciated fromthe following Detailed Description of the Invention, taken together withthe appended Figures briefly described below.

THE FIGURES

FIG. 1 illustrates in generalized block diagram form an embodiment of asystem for creating an object detector model using an enhanced versionof transfer learning to substantially reduce computational expenserelative to conventional methods.

FIG. 2 shows in generalized block diagram form an embodiment of aprocess for optimizing a teacher-student network in accordance with anaspect of the invention.

FIG. 3A illustrates a teacher-student optimization process in accordancewith an embodiment of the invention.

FIG. 3B illustrates a more generalized version of a teacher-studentoptimization process in accordance with a further embodiment of theinvention.

FIG. 4 illustrates in generalized block diagram form an embodiment of aprocess for active learning in accordance with an aspect of theinvention.

FIG. 5 illustrates in generalized block diagram form an embodiment of aprocess for generating synthetic images in accordance with an aspect ofthe invention.

FIG. 6A shows in generalized block diagram form an embodiment of theoverall system as a whole comprising the various inventions disclosedherein.

FIG. 6B illustrates in circuit block diagram form an embodiment of asystem suited to host a neural network and perform the various processesof the inventions described herein.

FIG. 7 illustrates in generalized flow diagram form an overall view of asystem comprising various processes that may be accessed by anembodiment of at least some aspects of the invention.

FIG. 8 shows an embodiment of a dashboard of a production system inaccordance with the invention.

FIGS. 9A and 9B show an embodiment of a user interface for creating anew custom model including a new detector in accordance with theinvention.

FIG. 10 illustrates an embodiment of a user interface screen for addingimages for training the new model in accordance with the invention.

FIG. 11A shows an embodiment of a user interface for defining a newobject of interest in accordance with the invention.

FIG. 11B shows an embodiment of a system-generated screen for avoidingduplication of objects of interest in accordance with the invention.

FIG. 12A shows an embodiment of a user interface for beginning theprocess of labeling images to identify detections in accordance with theinvention.

FIG. 12B shows an embodiment of screen of a user interface that enableslabeling of detections of the new object of interest.

FIG. 13 shows an embodiment of a screen of a user interface that enablesa user to initiate training of the new custom model.

FIG. 14A shows an embodiment of a screen of a user interface the showsthe results of an initial iteration of the training process of theinvention.

FIG. 14B shows an embodiment of a screen of a user interface whereby anoperator is enabled to confirm or correct labels applied by anembodiment of the system and process of the present invention.

FIG. 15A shows an embodiment of a screen of a user interface showingstill frame images selected by the system using the new custom model forpresentation to an operator in response to an operator query for the newobject.

FIG. 15B shows an embodiment of a screen of a user interface showingvideo snippets selected by the system using the new custom model forpresentation to an operator in response to an operator query for the newobject.

DETAILED DESCRIPTION OF THE INVENTION

The present invention enables a user to create an object detection modelfor custom objects, and to then use that custom model to find thoseobjects is video and still frame imagery where that imagery can beeither live or pre-recorded. In an embodiment of an aspect of theinvention, the training of the custom object detection model is achievedwith a volume of training data substantially less than in many prior artsystems. In an embodiment of a further aspect of the invention, thecustom model, together with a backbone object detection neural networkthat is pretrained on a variety of objects, forms the teacher portion ofa teacher-student ensemble network which permits development of anoptimized student object detection model with significantly improvedcomputational efficiency. In an embodiment, each of the networks is a“Single Shot Multibox Detector” or “SSD” neural network for thedetection task, with classification and regression performed at andrelative to anchor boxes, where, in at least some implementations, thepredefined, fixed grid of anchor boxes is spread uniformly through theimage. While the following description assumes a supervised learningmodel, those skilled in the art will recognize, once they have digestedthe teachings herein, that unsupervised learning can also be used in atleast some embodiments. In particular, if a model is “pre-trained” on alarge amount of video data, all using unsupervised data—basically “selfsupervision”, the amount of fine tuning that would be needed to build aspecific model would be significantly reduced. While such a generalpurpose model that will work for any scene requires substantial computepower and data storage, data with considerable redundancy will greatlyreduce compute and data storage needs considerably. Where the datasource is a specific camera or group of cameras, which is a commonconfiguration wherein a specific camera will see a highly regular scenewith a lot of redundancy, an unsupervised learning system can reduce thetuning time.

To develop a custom object detector model, a set of representativeimages of the object of interest is gathered. The images can come froman existing or newly captured dataset or, in some embodiments, can begenerated synthetically, as discussed in greater detail below inconnection with FIG. 5 . It is desirable that the images capture a rangeof textures, viewpoints, lighting, and occlusion conditions. To avoidbias in the detector, it is also desirable to use images that arerepresentative of the environment where the object is needed to belocated. For example, if the object of interest is a “red ball”, theimages will preferably comprise images shot at locations where such aball needs to be found, such as playing fields, etc.

Each of the images is then labeled by identifying all of the occurrencesof the object of interest and drawing a tight bounding box enclosing theentire object without extraneous elements. The minimum number of imagesfor generation of a model can vary depending upon the size of thedataset and the nature of the objects being sought, but is typicallybetween 10 and 1,000, with 50 images an exemplary number.

Once a sufficient quantity of images has been labeled, training isperformed by the associated SSD, which may be operating with any of avariety of backbones, for example Resnet50, Resnet34, InceptionV3, ornumerous other SSD variations, but, for at least some embodiments, withthe weights unfrozen so that the detectors can be fine tuned for aspecific task by propagating the gradient of the loss function from thetop to the bottom. The output of the SSD's comprises a first model. Thatmodel, together with an extensively trained system production model,comprise the “teacher” side of a teacher-student network, where theteacher networks are merged in an optimizing step using a novel form ofdistillation and the output of that step is a student model capable ofdetecting objects in all of the classes for which the system productionmodel is trained plus all classes that can be detected by the iteratedmodel. In some embodiments, no system model will have been previouslydeveloped. In such a case, the the event that no system production modelhas been developed,

The model is then tested against a set of images for validation, whichprovides an indication of how well the model performs. As discussedbelow, and depending upon the embodiment, various feedback and iterativetechniques can be implemented to improve the model. In at least someembodiments, it is desirable to provide interoperability between thesystem production model and the custom, or iterated, model. Thus, in anembodiment, the two teacher models use a common vocabulary of objectclasses, where an operator seeking to designate a new class can see thepreviously trained classes and thus avoid duplication. Further, in anembodiment, the models use the same deep neural network framework,although such commonality is not required in all embodiments. In otherembodiments, interoperability can be achieved where the neural networkmodels are understandable in both frameworks, for example using the ONNXformat although the ONNX format does not always yield successful resultswithout operator intervention. It will be appreciated by those skilledin the art that, if the networks are interoperable, the custom model canbe merged with the system production model. Further, should the systemproduction model yield poor results, for example as the result of poorlabeling, the images from the system production model can be supplied tothe image set of the present invention such that any labeling errors canbe corrected, resulting in a more accurate production model.

FIG. 1 shows in block diagram form a generalized view of an embodimentof a system for developing custom object detector models in accordancewith the invention. More specifically, FIG. 1 illustrates broadly howsuch a system is perceived by an operator, while FIG. 2 illustrates amore detailed view of the system from the process execution perspective.Referring particularly to FIG. 1 for now, the process begins at 110 bycollecting representative images of the object of interest that show theobject in the various contexts in which it might occur naturally asdiscussed above. In some instances where custom object detection modelsare desired, the operator of the system may have physical examples ofthe object, or an exemplary CAD model, or images taken out of context,and appropriate image datasets can be developed from such data usingsynthetic techniques as described below and described in connection withFIG. 5 . In some embodiments where the dataset is developed entirelysynthetically, no human involvement is required, whereas in otherembodiments the operator will select and provide the necessary images.

At step 115, the classes of objects are defined, for example, “redball”, or “sunflower”, or any other appropriate term. The descriptorsfor the class are assigned by the operator in many embodiments, althoughit will be appreciated that, if synthetic data is used, the object isalready defined and, as with step 110, no human involvement is required.Next, at step 120, at least some of the images from the collected imageset are labeled by applying bounding boxes tightly around eachoccurrence of the object in the images. While human intervention isrequired to applying bounding boxes for many types of images, for atleast synthetic images the labeling can be performed automatically,since the process of generating a synthetic image includes knowing wherethe object is within the image. Next, at step 125, the model is trainedby processing the labeled images in an appropriate neural network, wherethe result is an iterated model 130. The training process is typicallyan SSD as described above although in some instances a Low Shot Learningapproach can work to get to an iterated model faster with less labor inacquiring training data. Other types of deep learning networks suitablefor detecting objects in imagery are also acceptable. In an embodiment,the backbone or deep residual network of the SSD can be the Resnet50architecture, although architectures such as InceptionV3, Resnet34 withan additional layer for smaller objects, or any other functionallyequivalent architecture may also be acceptable.

The output of the iterated model 130 is a set of images and labelingdata, where the top layer classifier for the iterated model will havetwo outputs, specifically new-class versus background. That output issupplied to an optimization process 135, described in more detail inconnection with FIG. 3 , below and also supplied to a machine-assistedlabeling process 140 and an active learning process 145, both of whichreceive the images that remain unlabeled by the operator at step 120, asdiscussed in greater detail hereinafter. Generally, the machine assistedlabeling process 140 receives the unlabeled images from 200 and 205 and,based on input from the iterated model 130, evaluates those unlabeledimages and provides hints, or suggestions, as to what label or labelsshould be applied to each of the unlabeled images. Those hints orsuggestions are, after combination with the results of the activelearning process, returned to the queue of the images dataset beinglabeled at 120 to permit either a human or automated operator toconfirm, ignore or correct labels applied by the system to previouslyunlabeled images. The manner in which these suggestions are provided toan operator is discussed in greater detail hereinafter in connectionwith an embodiment of a user as shown in FIGS. 8-15B, and particularlyin connection with FIGS. 14A-14B.

Active learning 145, discussed in greater detail in connection withFIGS. 2 and 4 , tests the confidence, or lack thereof (“uncertainty”)that an image has been correctly labeled, then sorts the unlabeledimages (i.e., not labeled by an operator) from the iterated model 130according to their uncertainty value. A group of images having thegreatest uncertainty is then fed back to labeling step 120 forreconsideration by the operator, after being combined at step 170 withthe results of the machine-assisted labeling step 140. It will beappreciated by those skilled in the art that, because themachine-assisted labeling process 140 and the active learning process145 both evaluate the same unlabeled images in at least someembodiments, the function of the combining step 170 is to organize theoutput of those processes in a way that minimizes the effort required ofan operator to cause the model to yield acceptable results.

Through repetition of the cycle of labeling, training, creating theiterated model, testing for uncertainty, then sending the least certainimages back to the operator for reassessment, the model is iterativelyimproved. Because the uncertainty threshold or selection process can beadjusted according to any convenient criteria, the size of the group ofimages sent back for review by the operator can be comparatively smallcompared to the full dataset, with the result that a relatively smallvolume of images can, through iterative assessment, refine the iteratedmodel 130 until it achieves acceptable accuracy. This reduces the laborinvolved and can also reduce computational expense.

As noted above, the output of the iterated model 130 is also supplied toan optimization process 135, which also receives as an input the imagesand a system production model 150. The system production model 150 andthe iterated model 130 form the teacher pair of networks, where each istrained for different objects and, through optimization process 135,their trainings are combined into a single student model, specificallymerged model 155, trained to detect any object or objects that couldhave been detected by either (or both) the system production model orthe iterated model. The merged model will have (N+1)+1=(N+2) outputswhere the last “+1” is for the background class. Omitted from FIG. 1 forthe sake of simplicity, but discussed in greater detail hereinafter, isthat optimization step 135 also receives as inputs the training data forthe system production model and the iterated model, and also receives asan input the unlabeled images.

The output of the merged model is then deployed, step 160, where it isapplied to the production data 165. The results of that deployment arethen fed back to step 120, as were the images labeled by themachine-assisted labeling process 140 and the active learning process145, to allow the operator to correct the labeling of any images thatthe operator determines were mislabeled. It will be appreciated that,depending upon the embodiment, the feedback from one or more of thefeedback sources 140, 145 and 165 is optional.

Further, in implementations where the system production model stillneeds to be developed, the foregoing steps can be used to create thesystem production model simply by executing the above-described processsteps but without inclusion of the system production model and itsassociated dataset as inputs. As just one example, in an embodiment, thefirst execution of the process of the invention, including theaforementioned feedback as desired, classifies and detects a firstobject. That model, while capable of classifying and detecting only afirst object can be used as a nascent system production model, whereeach successive execution of the process adds an additional object tothe objects that can be detected by that developing system productionmodel. The collection of training data developed through successiveaddition of objects to the developing system production model becomesthe system production training dataset. For purposes of the presentinvention, the foregoing description of the development of the systemproduction model is not intended to be limiting, and the systemproduction model can be developed in any suitable manner, The followingdescription of the invention assumes a pre-existing system productionmodel unless specifically stated to the contrary, although it will beapparent to those skilled in the art, upon digesting the detailspresented hereinafter, how to modify those processes and systems todevelop the system production model if one does not yet exist.

Referring next to FIGS. 2 and 3A, as noted above FIG. 2 illustrates amore detailed view of the system from the process execution perspectivewhile FIG. 3A extracts from FIG. 2 the elements of a teacher-studentnetwork used to perform a version of optimization that is a novelapproach to distillation. The processes of FIG. 2 begin at 200, where aset of images initially comprises a dataset where at least some of theimages include an object of interest. The image set 200 initiallycomprises unlabeled images 200A, but, as explained further below, willeventually include both unlabeled images 200A and labeled images 200B,and ideally will eventually include only labeled images 200B. Eitheralternatively or in addition, one or more video snippets 205 alsocomprise a dataset where at least some of the frames of the videosnippets include an object of interest. As with images 200, initiallyall of the video snippets are unlabeled but eventually compriseunlabeled video snippets 205A and labeled video snippets 205B, and,ideally, ultimately only labeled snippets 205B.

To begin, in an embodiment a user assigns a name to an object ofinterest and then labels a batch of unlabeled images 200A. In someembodiments, the batch may range in size from about ten images to 1000or more images, at least partly based on the size of the production dataset. The images in the batch are then labeled, step 210, where step 120of FIG. 1 essentially comprises steps 200, 205 and 210, by tightlyenclosing in a bounding box each appearance of the objects of interestin each image, where the process of assigning a bounding box to adetected object is performed by a human operator, a previously trainednetwork, or other similar approach. The output of the labeling step 210for that batch forms training data 250. Once the training data image set250 includes all of the images from the batch, the deep learning networkis trained at step 125 by processing the training dataset 250.

The result of the training step 125 is the first iteration of iteratedmodel 130, which also functions as a teacher as discussed further belowand shown in simplified form in FIG. 3A. As shown in FIG. 1 anddiscussed there, this first iteration of the iterated model 130 issupplied to an Optimize process, step 135, which performs a novel formof distillation, and is also supplied to a machine-assisted labelingprocess, step 140 and an active learning process, step 145, as touchedupon above and discussed in greater detail hereinafter. Themachine-assisted label process 140 and the active learning process 145each receive the remaining unlabeled still frame images and videosnippets from image sets 200 and 205, and, after processing as describedin greater detail below, the results of those processes are combined atstep 170 and fed back to the queue of images and video snippets in 200and 205, where images are then provided to the user for review based atleast in part on uncertainty scores.

Still referring to FIG. 2 but also as seen in simplified form in FIG.3A, in addition to receiving the iterated model 130, the optimizeprocess, step 135, also receives the unlabeled images and unlabeledvideo frames from image sets 200A and video set 205A, as well as thesystem production model training dataset 260, the training dataset 250(which can in some embodiments be the same as 260, for example if thesystem production model 150 had been trained to detect faces while theclient-generated custom model 130 was trained to detect faces plusbodies), and also receives as an input the system production model 150which has been trained to detect many more classes than iterated model130 is trained to recognize. The dataset 260 can be any of a widevariety of datasets, for example MS-COCO, OpenImages, or any pre-labeleddataset including privately developed datasets. Further, a group of newimages and/or videos comprising new unlabeled data 255 from anyconvenient image set and not necessarily related to the images or videos200/205, provides a further input to the iterated model 130, the systemproduction model 150, as well as the optimize step 135. The optimizationstep 135 implements a teacher-student network to perform knowledgedistillation, where the optimization performed at step 135 combines thedetection capabilities of iterated model 130 and system production model150 as teachers 300 and provides that distilled knowledge to mergedmodel 155, or student 305, as discussed in greater detail in connectionwith FIG. 3A and described in more general terms in connection with FIG.3B, below.

Still referring to FIG. 2 , the distillation performed by theoptimization step results in the merged model being able to detect notonly the classes of objects of the system production model, but also theclass(es) of objects added by the customer. The merged model is then runagainst the production data 165, typically comprising a larger set ofunlabeled images and video frames than the initial batch 200A. Theproduction data, now labeled, is then provided to the operator to formpart of image set 200B. Likewise, the images fed back from processes 140and 145 are included in the labeled image set 200B. The labeled imagesare then presented to the operator at step 210, to permit the operatorto correct any labeling errors that resulted from any of steps 140, 145and 165, or to add any bounding boxes for objects that were missed on anearlier iteration.

In an additional aspect of some embodiments of the invention, trackingof video snippets, indicated at 270 in FIG. 2 , can be provided toreduce the number of images to be labeled, thus reducing both labor andcomputational expense. In an embodiment, a video snippet comprises aseries of sequential frames of an object, although not necessarily theobject of interest. Objects in video, at a reasonable frame rate, haveredundancy in their appearance as well as spatial position. The numberof frames in a snippet varies according to how long invariant featuresof the object can be identified in successive frames, as taught in therelated applications identified above. By selecting one of the frames ofthe snippet for labeling as shown at step 210, the location of theobject in the remaining frames of the snippet can be automaticallycalculated and the object labeled by the tracking process 270. Thelabeled video snippet can then be processed in the same manner as stillimages from image set 200, including receiving feedback from any ofsteps 140, 145 and 165, followed by correcting any mislabeling or addingbounding boxes for missed objects. Any convenient algorithm for trackingcan be used, for example some single-shot learning based frameworks canallow the system to learn a detection model from a single labeledinstance. The trained model effectively outputs parameters of a detectorof a given instance that can be used to detect similar looking objectsin subsequent frames. In order to track multiple objects in the scene, aset of these detectors is used to individually track the bounding boxesand drop the detections when the detection score is lower than somethreshold. The threshold can be set in any convenient manner, forexample empirically, through experimentation, via a preset threshold,and so on.

Referring again to FIG. 3A, FIG. 3A extracts from FIG. 2 the elementscomprising the teacher-student network that facilitates a form ofdistillation whereby two or more teacher models, trained for differentclasses of objects, are combined into a single student model that isable to detect all of the objects of both teacher models. Conventionalensemble networks allow for redundancy, and essentially average thepredictions made by the constituent neural networks, resulting inimproved accuracy but with high computational cost because traditionalensemble networks run multiple neural networks first. Distillationallows transfer of knowledge from the large network (which can bethought of as a “teacher”) to a simpler “student” network, preservingaccuracy while reducing computational costs. The training indistillation algorithms occurs by running inference for “teacher”networks on their respective training data, and using their responses assoft labels (or targets) for training the “student” network. For labeleddata both the hard labels (ground truth) and the soft labels are usedfor training. In principle, a student model can also be trained from ateacher model from sufficiently representative unlabeled data only.

While distillation is known where the task is to classify an image intodifferent categories, the present invention extends this concept to adetection task where the model is required to report not only whether anobject of a particular class exists in an image, but also the locationof that object in the image, with the location typically represented aswithin a tight bounding box around the object. In an embodiment, thepresent invention enables combining two or more teacher models trainedfor different objects into a single student model containing all theobjects, and also enables using only partially labeled datasets to traina model. That is, at least some embodiments of the invention enableusing different sets where only a single one or only some objects ofinterest are labeled, thus saving substantial effort in that it becomesunnecessary to review all the data and relabel all the objects in allthe images.

Thus, in FIG. 3A, teacher networks 300 comprise the system productionmodel 150 and the iterated model 130, or just two teacher networks whoseknowledge will be merged into merged model 155, or student 305, throughthe optimize process 135. To perform the optimization, the training dataof the system production model, 260, as well as training data 250 andnew unlabeled data 255 serve as inputs to the optimize process 135 alongwith each of the teacher networks 300. The process of merging thoseteacher networks, run against the datasets 250, 255 and 260, to createthe student detector network 305 can be better understood from thefollowing discussion of classification and regression.

In an embodiment of the invention, a “Single Shot Multibox Detector”(SSD) neural network is used for the detection task. Classification andregression are performed at and relative to predefined, fixed grid ofboxes called “anchor boxes”. For a large set of “anchor boxes” spreaduniformly through the image, the SSD algorithm trains a network toperform two tasks, classification and regression, where classificationis determining the probability distribution of the presence of any ofthe objects of interest, or the background at an anchor box andregression is determine the bounding box of the object that is detectedat the anchor box.

Classification is modeled as a softmax function to output confidence ofa foreground class or the background class:

${P( C_{k} \middle| X )} = \frac{\exp( C_{k} )}{{\sum\limits_{j}{\exp( C_{j} )}} + {\exp(B)}}$

for foreground classes C_(k) and background class B, for the anchor boxX. Note here that background is treated just as one of the class amongstall the classes modeled by the softmax function. The background class istrained by extracting negative examples around the positive examples inthe labeled images. The loss function for training the classifier is across-entropy loss defined for every association of anchor box to alabel denoted by X_(Label,Anchor) [Eq. 1, below]:

${L_{conf}( {C,X} )} = {{- {\sum\limits_{x \in {Pos}}^{N}{\sum\limits_{Label}^{Clases}{x\begin{matrix} \\ \\{{Label},{Anchor}}\end{matrix}{\log( {P( C_{Label} \middle| X )} )}}}}} - {\sum\limits_{x \in {Neg}}^{N}{\log( {P( C_{B} \middle| X )} )}}}$

Regression is modeled as a non-linear multivariate regression functionthat outputs a four-dimensional vector representing center coordinates,width and height of the bounding box enclosing the object in the image.The loss function for training regressor is a smoothL1 loss functionL_(loc)(C,X). Only foreground objects are used for training theregressor as background class has no boundaries [Eq. 2, below]:

${L_{loc}( {C,X} )} = {\sum\limits_{x \in {Pos}}^{N}{x_{{Label},{Anchor}}{{smooth}_{L1}( {{\Delta x_{Box}} - {\Delta x_{Pred}}} )}}}$

Here x_(Label,Anchor) is 1 for an association between a positive labeland a predefined anchor box. Δx_(Box) is the offset of the ground truthlabel relative to the associated anchor box, Δx_(Pred) is the predictedbounding box from the network.

For training a standard SSD[3] model, parameters are learned thatminimize L_(conf)(C,X)+L_(loc)(C,X) defined over a selective set ofpositive and negative anchor boxes X, chosen using the using the labelsfrom manually annotated images. These labels are called hard labels withone-hot encoding for positive samples.

As a part of the workflow for the present invention, an operator willtrain multiple detectors by labeling multiple sets of data where only aparticular object of interest is labeled in each dataset. Distillationenables an operator to train a single student model from multipleteacher models without losing accuracy, and without requiring theoperator to label all the objects on all the datasets. The advantage ofdoing this is the performance gain resulting from running a singledetector instead of multiple detectors.

The teacher in this case constitutes multiple networks of similarcomplexity, where each network is able to detect a new class of objectas trained by the user. The student is a new network of similarcomplexity as the teacher models, where the goal is to distill theknowledge from multiple teacher models into a single student model.

While the distillation process can be performed on any number of teachernetworks, as an example, the algorithm can be illustrated by using twoteacher networks M₁ and M₂ to train a student network M. The teachernetworks are trained to detect class C₁ and C₂ with the respective“background” classes B₁ and B₂. “Background”, in this context, meansregions that do not contain the object of interest. (Labeled-Data)₁ and(Labeled-Data)₂ are employed for training M₁ and M₂ that have only theirrespective classes labeled.

In an embodiment, the student model is a single deepnet model M with twoclasses and a single background class B that is an intersection ofclasses B₁ and B₂. The probability mapping for the combined model can beperformed as follows. For the input X, the model for (Labeled-Data)1 and(Labeled-Data)2 has class probability as P(C₁|M₁,X) and P(C₂|M₂,X)respectively. Corresponding background probabilities are P(B₁|M₁,X) andP(B₂|M₂,X) respectively. The probabilities for the teacher models arecomputed as follows:

P _(Teacher)(B|X)=P(B ₁ |M ₁ ,X)×P(B ₂ |M ₂ ,X)

P _(Teacher)(C ₁ |X)=P(C ₁ |M ₁ ,X)×P(B ₂ |M ₂ ,X)

P _(Teacher)(C ₂ |X)=P(C ₂ |M ₂ ,X)×P(B ₁ |M ₁ ,X)

In this example, the loss terms for training the SSD comprise a lossterm for the classifier and a term of the regressor, shown in Eq. 1 andEq. 2, above. In the present invention, the loss function for training astudent model is a linear combination of two loss functions:

-   -   Loss1: Positive labels are hard labels that are extracted from        (Labeled-Data)₁ and (Labeled-Data)₂ where only positive labels        are sampled and no negative samples are extracted because it        isn't known whether a negative sample for class C₁ has a class        C₂ object (and vice-versa).        -   a. For training the classifier, only positive examples are            used in the cross-entropy loss in Eq. 1, above.

${{Loss}1_{conf}( {C,X} )} = {- {\sum\limits_{x \in {Pos}}^{N}{\sum\limits_{Label}^{Clases}{x_{{Label},{Anchor}}{\log( {P( { C_{Label} \middle| M ,X} )} )}}}}}$

-   -   -   -   Here x_(Label,Anchor) is 1 for the correct class and 0                for rest of the classes.

        -   b. For training the regressor, only bounding boxes for            positive labels are required. The smooth-L1 loss is used, as            defined in the loss function, Eq. 2, above

    -   Loss2: For each object, extract a quantity (for example, 400) of        the top detection bounding boxes Pos₁ and Pos₂ with a score        greater than 0.01 both from model M₁ and M₂ respectively:        -   a. These are soft labels for the SSD classifier and are used            as cross-entropy loss for training the classifier. Instead            of using hard binary targets, soft targets are used in the            cross entropy loss for training student model M

${{Loss}2_{conf}( {C_{1},C_{2},X} )} = {{- {\sum\limits_{x \in {Pos}}^{N}{{P_{Teacher}( C_{1} \middle| X )}{\log( {P( { C_{1} \middle| M ,X} )} )}}}} - {\sum\limits_{x \in {Pos}}^{N}{{P_{Teacher}( C_{2} \middle| X )}{\log( {P( { C_{2} \middle| M ,X} )} )}}} - {\sum\limits_{x \in {Neg}}^{N}{{P_{Teacher}( B \middle| X )}{\log( {P( { B \middle| M ,X} )} )}}}}$

-   -   -   b. For training the regressor, for each sample, compute the            regression target by weighing the smooth-L1 loss by the            classification score:

${{Loss}2_{loc}( {C_{1},C_{2},X} )} = {{\sum\limits_{x \in {Pos}_{1}}^{N}{{P_{Teacher}( C_{1} \middle| X )}{{smooth}_{L1}( {{\Delta x_{{Box},{Pos}_{1}}} - {\Delta x_{Pred}}} )}}} + {\sum\limits_{x \in {Pos}_{2}}^{N}{{P_{Teacher}( C_{2} \middle| X )}{{smooth}_{L1}( {{\Delta x_{{Box},{Pos}_{2}}} - {\Delta x_{Pred}}} )}}}}$

Here, X represents the anchor box associated to positive soft labels andΔx represents the difference between the soft label and the associatedanchor box X. So a highly confident classification score will have moreinfluence in optimizing the corresponding regression loss (smooth_(L1)loss). A bounding box that does not have a high confidence C₁ or C₂ boxwill be most likely a background and will not have any significantinfluence on the regression function.

The combined loss is α*Loss1+(1−α)*Loss2, where α is used to control theweights of the combined loss and, in an embodiment, is set to 0.25. Notethat any amount of representative unlabeled data can also be used totrain a student model from the teacher models M₁ and M₂. There, only theLoss2 term is employed, as there are only soft labels from the models,and no hard labels as used in the Loss1 term.

Referring next to FIG. 3B, there is shown therein a generalized andexpanded approach to the teacher-student optimization process of FIG.3A. In FIG. 3B, models M₁, M₂ through M_(N) comprise N teacher models325, 330 and 335, each of which is trained with unlabeled data 320, andN labeled data sets D₁, D₂ through D_(N), shown at 350, 355 and 360. Theoutputs of the models 325, 330 through 335, along with new unlabeleddata 320 as well as data sets 350, 355 through 360 are all provided tothe optimize process 365, where the loss terms for training the SSD are,as above, comprised of a loss term for the classifier and a term of theregressor where each of those terms is analogous to that discussed inconnection with FIGS. 2 and 3A.

Referring next to FIG. 4 , the active learning function, shown asprocess 145 in FIGS. 1 and 2 and implemented in some embodiments of theinvention, can be better appreciated. Data labeling is important butvery time consuming for operators. The active learning aspect of thepresent invention enables operators to build a model with the leastvolume of labeled data. In an embodiment, an operator labels a smallrandom batch of the data and that small batch is then used to train aninitial model. The resulting model is then used to create an uncertaintyscore for each of the remaining unlabeled data. In object detectortraining, the uncertainty score is defined as the average entropy of theanchor box classification

${UC}_{score} = {\frac{1}{N}{\sum\limits_{i = 0}^{N - 1}{\sum\limits_{j = 0}^{C - 1}{- {{Pijlog}({Pij})}}}}}$

The system organizes the unlabeled data according to each datum'suncertainty score, after which the operator is invited to label a batchof the unlabeled data having the highest uncertainty scores. The modelis then retrained using all of the labeled data, yielding an improvedresult. This cyclic process of labeling, training and querying iscontinued until the model converges or the validation accuracy is deemedsatisfactory by the user. By using active learning, the customers areable to train a model with high accuracy by only labeling a small subsetof the raw data, for example as few as ten images for some models and asmany as 1000 images or more for other models, based at least in part onthe size of the dataset.

FIG. 4 illustrates the iterative approach described above, where arandom sample of the image data, for example from 200A of FIG. 2 , islabeled by an operator at 400, substantially as shown at 210 in FIG. 2 .The labeled random sample of images is then provided at step 405 astraining data (e.g., 250 in FIG. 2 ) and is used to train the deeplearning network, step 410. The training results in an iteration ofmodel 415, and the model 415 is run against the unlabeled images 420,such as the unlabeled images in data sets 200 and 205 where anuncertainty score is assigned to each image as described above. Theimages are organized by their uncertainty scores, 430, and at least abatch of those unlabeled images having the highest uncertainty scores(i.e., lowest certainty that the labeling is correct) is fed back to theoperator to confirm or correct the labeling, including labeling a missedimage, step 435. The number of images in the batch fed back to step 435can be determined in any convenient manner, for example by using apreset number, or by assigning a threshold above which the image isreturned for operator review and relabeling, empirically, or by any of awide variety of other approaches. The size of the batch can also varywith the iteration, as the model converges. As better seen in FIG. 14B,where a queue of images is provided to the operator, those images forwhich further review is particularly suggested can be indicated bydelineating a threshold in the user interface. In at least someembodiments, the model will converge to an acceptable accuracy where,for each iteration, the operator need only review and confirm or correctthe labeling of those images above the threshold mark

In some instances, the object is available physically but there areinsufficient images of the object in context, i.e., with an appropriatebackground, to create a dataset adequate to train a model to yieldsufficiently accurate results. In other cases, no physical exampleexists, but a 3D computer model is available. In such circumstances, thegeneration of synthetic images can offer a number of advantages. Anembodiment of such an approach can be appreciated from FIG. 5 , where aphysical object or its computer model is available but out of context orin insufficient examples of context. If a physical example of the objectof interest 500 is available, the object can be scanned in various ways,including LiDAR and appropriate post-processing, 510, a visible lightimage scan plus SLAM (Simultaneous localization and mapping) processing,515, or a time-of-flight (ToF) generated model, 520. The scan of theobject, which can be created by a combination of any of theseapproaches, results in the object's 3D geometry and surface textures andcolors, 525. Alternatively, if no physical example of the object exists,but a CAD model either exists or can be created, 530, that, too, canyield the object's details.

The details of the object are then provided from 525 to a blendingprocess, 535, which also receives data representative of at least color,tone, texture and scale of the scene depicted in a background image,540, as well as characterizing information specifying position and angleof view of a virtual camera, 545, together with characteristics of thevirtual camera such as distortion, foreshortening, compression, etc. Thevirtual camera can be defined by any suitable digital representation ofa model of camera. The process 535 modifies the object in accordancewith the context of the background image, including color and texturematching as well as scaling the object to be consistent with itslocation in the background image, and adjusts the image of the object bywarping, horizontally or vertically tilting the object, and othersimilar photo post-processing techniques to give the syntheticrepresentation of the object proper scale, perspective, distortionrepresentative of the camera lens, noise, and related cameracharacteristics. The blended and scaled object image from step 555 isthen provided to a renderer 560 which places the blended and scaledobject into the background image. To achieve that result, the renderer560 also receives the background image 540 and the camera information,545 and 550. The result is a synthetic image 565 of the object in thebackground image, usable in dataset 200 of FIG. 2 . The process of FIG.5 can be repeated as many times as necessary to generate a complete butsynthetic image dataset, where each image is different as the result ofa changed background image, a different angle of view, a differentcamera position, etc. However, unlike non-synthetic images, in syntheticimages the location of the object is known, and thus the labeling stepof FIG. 2 can be performed automatically rather than requiring anyaction by a human operator. This permits fully automatic operation of atleast the initial training of the system of FIG. 2 , and in someinstances eliminates the need for either machine assisted labeling andactive learning, although verification that the production data has beenproperly labeled may still benefit from review by a human operator.

Referring next to FIG. 6A, shown therein is a generalized view of anembodiment of a system 600 that executes the various processes that,together, comprise the various inventive aspects described herein. Insuch an embodiment, the system 600 comprises a user device 605 having auser interface 610. A user of the system communicates with a multisensorprocessor 615 either directly or through a network connection which canbe a local network, the internet, a private cloud or any other suitablenetwork. The multisensory processor, described in greater detail inconnection with FIG. 6B, receives input from and communicatesinstructions to a sensor assembly 625 which further comprises sensors625A-625 n. The sensor assembly can also provide sensor input to a datastore 630, and in some embodiments can communicate bidirectionally withthe data store 630.

Next with reference to FIG. 6B, shown therein in block diagram form isan embodiment of the multisensor processor system or machine 615suitable for executing the processes and methods of the presentinvention. In particular, the processor 615 of FIG. 6B is a computersystem that can read instructions 635 from a machine-readable medium orstorage unit 640 into main memory 645 and execute them in one or moreprocessors 650. Instructions 635, which comprise program code orsoftware, cause the machine 615 to perform any one or more of themethodologies discussed herein. In alternative embodiments, the machine615 operates as a standalone device or may be connected to othermachines via a network or other suitable architecture. In a networkeddeployment, the machine may operate in the capacity of a server machineor a client machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. In someembodiments, system 600 is architected to run on a network, for example,a cloud network (e.g., AWS) or an on-premise data center network.Depending upon the embodiment, the application of the present inventioncan be web-based, i.e., accessed from a browser, or can be a nativeapplication.

The multisensor processor 615 can be a server computer such asmaintained on premises or in a cloud network, a client computer, apersonal computer (PC), a tablet PC, a set-top box (STB), a personaldigital assistant (PDA), a cellular telephone, a smartphone, a webappliance, a network router, switch or bridge, or any machine capable ofexecuting instructions 635 (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” is to be understood toinclude any collection of machines that individually or jointly executeinstructions 635 to perform any one or more of the methods or processesdiscussed herein.

In at least some embodiments, the multisensor processor 615 comprisesone or more processors 650. Each processor of the one or more processors650 can comprise a central processing unit (CPU), a graphics processingunit (GPU), a digital signal processor (DSP), a controller, one or moreapplication specific integrated circuits (ASICs), one or moreradio-frequency integrated circuits (RFICs), or any combination ofthese. In an embodiment, the machine 615 further comprises static memory655 together with main memory 645, which are configured to communicatewith each other via bus 660. The machine 615 can further include one ormore visual displays as well as associated interfaces, all indicated at665, for displaying messages or data. The visual displays may be of anysuitable type, such as monitors, head-up displays, windows, projectors,touch enabled devices, and so on. At least some embodiments furthercomprise an alphanumeric input device 670 such as a keyboard, touchpador touchscreen or similar, together with a pointing or other cursorcontrol device 675 such as a mouse, a trackball, a joystick, a motionsensor, a touchpad, a tablet, and so on), a storage unit ormachine-readable medium 640 wherein the machine-readable instructions635 are stored, a signal generation device 680 such as a speaker, and anetwork interface device 685. A user device interface 690 communicatesbidirectionally with user devices 620 (FIG. 6A). In an embodiment, allof the foregoing are configured to communicate via the bus 660, whichcan further comprise a plurality of buses, including specialized buses,depending upon the particular implementation.

Although shown in FIG. 6B as residing in storage unit ormachine-readable medium 640, instructions 635 (e.g., software) forcausing the execution of any of the one or more of the methodologies,processes or functions described herein can also reside, completely orat least partially, within the main memory 645 or within the processor650 (e.g., within a processor's cache memory) during execution thereofby the multisensor processor 615. In at least some embodiments, mainmemory 645 and processor 650 also can comprise, in part,machine-readable media. The instructions 635 (e.g., software) can alsobe transmitted or received over a network 620 via the network interfacedevice 685.

While machine-readable medium or storage device 640 is shown in anexample embodiment to be a single medium, the term “machine-readablemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, or associated caches andservers) able to store instructions (e.g., instructions 635). The term“machine-readable medium” includes any medium that is capable of storinginstructions (e.g., instructions 635) for execution by the machine andthat cause the machine to perform any one or more of the methodologiesdisclosed herein. The term “machine-readable medium” includes, but isnot limited to, data repositories in the form of solid-state memories,optical media, and magnetic media. The storage device 640 can be thesame device as data store 630 (FIG. 6A) or can be a separate devicewhich communicates with data store 630.

FIG. 7 illustrates, at a high level, an embodiment of the softwarefunctionalities implemented in an exemplary system 600 shown generallyin FIG. 6A, including an embodiment of those functionalities operatingin the multisensor processor 615 shown in FIG. 6B. Thus, inputs 700A-700n can be video or other sensory input from a drone 700A, from a securitycamera 700B, a video camera 700C, or any of a wide variety of otherinput device 700 n capable of providing data sufficient to at leastassist in identifying an animate or inanimate object. It will beappreciated that combinations of different types of data can be usedtogether for the analysis performed by the system. For example, in someembodiments, still frame imagery can be used in combination with videofootage. In other embodiments, a series of still frame images can serveas the gallery. Still further, while organizing the input feedchronologically is perhaps the most common, arranging the input dataeither by lat/long or landmarks or relative position to other datasources, or numerous other methods, can also be used in the presentinvention. Further, the multisensor data can comprise live feed orpreviously recorded data. The data from the sensors 700A-700 n isingested by the processor 615 through a media analysis module 705. Inaddition to the software functionalities operating within themultisensor processor 615, described in more detail below, the system ofFIG. 7 comprises encoders 710 that receive entities (such as facesand/or objects) and activities from the multisensor processor 615.Further, a data saver 715 receives raw sensor data from processor 615,although in some embodiments raw video data can be compressed usingvideo encoding techniques such as H.264 or H.265. Both the encoders andthe data saver provide their respective data to the data store 630 inthe form of raw sensor data from data saver 710 and faces, objects, andactivities from encoders 705. Where the sensor data is video, the rawsensor data can be compressed in either the encoders or the data saverusing video encoding techniques, for example, H.264 & H.265 encoding.

Where the multisensor data from inputs 700A-700 n includes full motionvideo from terrestrial or other sensors, the processor 615 can, in anembodiment, comprise a face detector 720 chained with a recognitionmodule 725 which comprises an embedding extractor, and an objectdetector 730. In an embodiment, the face detector 720 and objectdetector 730 can employ a single shot multibox detector (SSD) network,which is a form of convolutional neural network. SSD'scharacteristically perform the tasks of object localization andclassification in a single forward pass of the network, using atechnique for bounding box regression such that the network both detectsobjects and also classifies those detected objects. Using, for example,the FaceNet neural network architecture, the face recognition module 725represents each face with an “embedding”, which is a 128-dimensionalvector designed to capture the identity of the face, and to be invariantto nuisance factors such as viewing conditions, the person's age,glasses, hairstyle, etc. Alternatively, various other architectures, ofwhich SphereFace is one example, can also be used. In embodiments havingother types of sensors, other appropriate detectors and recognizers maybe used. Machine learning algorithms may be applied to combine resultsfrom the various sensor types to improve detection and classification ofthe objects, e.g., faces or inanimate objects. In an embodiment, theembeddings of the faces and objects comprise at least part of the datasaved by the data saver 710 and encoders 705 to the data store 630. Theembedding and entities detections, as well as the raw data, can then bemade available for querying, which can be performed in near real time orat some later time.

Queries to the data are initiated by analysts or other users through auser interface 735 which connects bidirectionally to a reasoning engine740, typically through network 620 (FIG. 6A) via a web servicesinterface 745, although in some embodiments the data is all local andthe software application operates as a native app. In an embodiment, theweb services interface 745 can also communicate with the modules of theprocessor 615, typically through a web services external systeminterface 750. The web services comprise the interface into the back-endsystem to allow users to interact with the system. In an embodiment, theweb services use the Apache web services framework to host services thatthe user interface can call, although numerous other frameworks areknown to those skilled in the art and are acceptable alternatives.Likewise, the system can be implemented in a local machine, which mayinclude a GPU, so that queries from the UI and processing all execute onthe same machine.

Queries are processed in the processor 615 by a query process 755. Theuser interface 735 allows querying of the multisensor data for faces andobjects (collectively, entities) and activities. One exemplary query canbe “Find all images in the data from multiple sensors where the personin a given photograph appears”. Another example might be, “Did John Doedrive into the parking lot in a red car, meet Jane Doe, who handed him abag”. Alternatively, in an embodiment, a visual GUI can be helpful forconstructing queries. The reasoning engine 740, which typically executesin processor 615, takes queries from the user interface via web servicesand quickly reasons through, or examines, the entity data in data store630 to determine if there are entities or activities that match theanalysis query. In an embodiment, the system geo-correlates themultisensor data to provide a comprehensive visualization of allrelevant data in a single model. Once that visualization of the relevantdata is complete, a report generator module 760 in the processor 615saves the results of various queries and generates a report through thereport generation step 765. In an embodiment, the report can alsoinclude any related analysis or other data that the user has input intothe system.

The data saver 715 receives output from the processing system and savesthe data on the data store 630, although in some embodiments thefunctions may be integrated. In an embodiment, the data from processingis stored in a columnar data storage format, such as Parquet as just oneexample, that can be loaded by the search backend and searched forspecific embeddings or object types quickly. The search data can bestored in the cloud (e.g. AWS S3), on premise using HDFS (HadoopDistributed File System), NFS, or some other scalable storage. In someembodiments, web services 745 together with user interface (UI) 735provide users such as analysts with access to the platform of theinvention through a web-based interface. The web based interfaceprovides a REST API to the UI. The web based interface, in turn,communicates with the various components with remote procedure callsimplemented using Apache Thrift. This allows various components to bewritten in different languages.

In an embodiment, the UI is implemented using React and node.js, and isa fully featured client side application. The UI retrieves content fromthe various back-end components via REST calls to web service. The UserInterface supports upload and processing of recorded or live data. TheUser Interface supports generation of query data by examining therecorded or live data. For example, in the case of video, it supportsgeneration of face snippets from uploaded photograph or from live video,to be used for querying. Upon receiving results from the ReasoningEngine via the Web Service, the UI displays results on a webpage.

A user interface comprises another aspect of the present invention, andvarious screens of an embodiment of a user interface are shown in FIGS.8-15 . In particular, FIG. 8 shows an opening screen of a productionsystem 800, typically the source of the system production model 150 andthe system production model training data 260. As discussed above, in atleast some embodiments the production system 800 is trained on a widevariety of classes of objects. Nevertheless, an operator may find ituseful to identify an object that is not included among those for whichthe production system 800 has been trained. In such a circumstance, thescreen of an embodiment of the user interface (sometimes “UI”hereinafter) shown in FIG. 8 permits the operator to “Add New Model”,shown at 805. By clicking on that link, the embodiment of a userinterface screen 900 shown in FIG. 9 appears. In that screen, a list ofthe existing detectors 905 is shown, to permit the avoidance ofduplication. In an embodiment, parameters of each detector are shown,for example model accuracy 910, date the model was last deployed 915,and model creation date 920, although such a display can optionallycomprise more or fewer such parameters depending upon theimplementation.

If the operator decides that the existing models would not yield thedesired results, the operator can click on “New”, shown at 925, in whichcase in an embodiment a screen such as shown in FIG. 9 appears. Theoperator is then enabled to designate a new detector, 950. In aconventional manner, the operator can correct an error in designation bycanceling, 960, but if the operator is satisfied then the new detectoris created by clicking on “Create Detector” at 965. Upon creation of thenew detector, a UI screen 1000 such as shown in FIG. 10 invites theoperator to add an image set, e.g., image sets 200 and 205, either froman addressable drive 1005, which is indicated as local in FIG. 10 butcan be local or remote, or from an existing dataset 1010, or both. Thisprovides the collection of unlabeled images that can be labeled by theoperator at step 210 (FIG. 2 ). However, before labeling can begin, theoperator needs to define a new object, as shown in exemplary form in theUI screen 1100 of FIG. 11A, which opens once the image set chosen inFIG. 10 is loaded. Thus, for example, the image data set loaded in FIG.10 may be named “Redball1” as shown at 1105. Parameters of the data set1105 then appear on the screen at 1110, and can include, in an exemplaryembodiment, the number of images in the dataset (73 for the example ofRedball1), as well as the number that have been labeled and thus areready to be used for training (zero in FIG. 11A since no labeling hasyet occurred), and the number actually used for training (again zero atthis stage).

The operator is invited to define a new object by clicking on “NewObject”, 1115, which causes, in an embodiment, the screen 1120 of FIG.11B to be displayed to the operator. The new object is defined by theoperator as shown at 1115, and for purposes of this example isdesignated “Redball”. Also shown on the screen 1120 is a list 1155 ofthe objects that already are identified in the Production System (FIG. 8). The new object is added by clicking on the “Create Object” field,1160, which brings up a screen such as the exemplary version 1200 shownin FIG. 12A. At this point, the image data set 1105 is available, andthe new object 1115 is defined, so the operator is invited to beginlabeling a random sample of the images in the dataset 1105 by clickingon “Label”, shown at 1205, which, in an embodiment, brings up the screenshown in FIG. 12B.

When the screen 1250 of FIG. 12B is displayed, the operator is presentedwith a queue of images as indicated generally at 1255. For each of theimages that includes the object of interest, a “red ball” 1115 in thiscase, the operator encloses the object by forming a bounding box tightlyaround the object, as shown at 1260. Once each appearance of an objectof interest in the image is enclosed in a box 1260, the image can besubmitted for inclusion in the group of images used for initialtraining. Accuracy in such labeling is important, both to ensure thateach instance of the object of interest is identified, and also toensure that the boxes only enclose all or at least some portion of theobject of interest. However, as discussed elsewhere herein, inembodiments which comprise in part one or both of machine labeling andactive learning, the operator will be provided an opportunity to correctany labeling errors or omissions. Once a suitable number of images arelabeled, where, as noted above, that number can vary depending on theparticular object and model, the process advances to the screen of FIG.13 , indicated generally at 1300.

In FIG. 13 , the values at 1110 have changed from FIG. 12A, because nowa number of images have been labeled and are available to begin trainingof the iterated model 130. Thus, in the example of FIG. 13 , of theseventy-three images available, thirty-eight have been labeled,thirty-five remain unlabeled, and, so far, no images have been used intraining. Training of the model 130 begins by clicking on the “Train”field 1305. This starts the training process described in FIGS. 1-4 ,above, including the optimize process 135, the machine-assisted process140 and the active learning process 145. The result can be seen in FIG.14A, denoted generally at 1400, which depicts an embodiment of a screenshowing the results of a training iteration. In part, the results can beseen from the changes in the values at 1110, where now thirty-eightimages have been used for training, none remain available for training,and, for the embodiment shown, seventeen have been fed back via step 170for consideration by the operator. In other embodiments, the number ofimages for review can be the combination of the images that remainedunlabeled after the labeling step, or thirty-five, plus the seventeenreturned from step 170, yielding a total of fifty-two instead ofseventeen. In at least some embodiments, the top of the queue ofunlabeled images for which operator review is suggested will comprisethe images received back from the machine-labeling and active learningprocesses 140 and 145, respectively. That queue of images can be betterappreciated from the user interface screen shown in FIG. 14B and denotedgenerally at 1450, where the queue is indicated at 1455. Images with thehighest uncertainty scores are at the top of the queue, where at 1460 athreshold is indicated. The threshold 1460 indicates that the operatoris particularly invited to review the images above that threshold sincethose images have the highest uncertainty values. The labels proposed bythe active learning and machine-assisted labeling process can beappreciated from an image 1465, shown in the queue 1455 and also inlarger size, at the right in the embodiment of FIG. 14B, when selectedfor review. In the image 1465, some, though not all, of the objects aretightly enclosed by dashed boxes 1470, indicating that the label is aproposed label. As discussed hereinafter, any drawing style for theboxes 1470 is acceptable although preferably the boxes indicatingproposed labels are readily distinguishable from boxes applied by theoperator, or, as discussed below in connection with FIGS. 15A-15B, boxesindicating various levels of confidence that an image satisfies a querybased on the new model. Referring still to FIG. 14B, the operator isinvited to confirm the suggested labeling, either by clicking on the boxor any other convenient form of selection. If the operator chooses toreject the selection, in an embodiment the selection can simply beignored, or in other embodiments the specific box 1470 can be selectedby a different selection process that indicates the proposed label isrejected, such as by a delete key as just one of many options. Ininstances where there is no pre-existing system production model, asdiscussed above, the foregoing process can be used to develop the systemproduction model.

Depending upon the embodiment, the process of FIG. 2 iterates as theimages in the queue 1455 of FIG. 14B as reviewed, although in otherembodiments the next iteration is only performed after a batch of imagesis reviewed, with, as just one example, all of the images above thethreshold 1460 being considered a batch. As noted above, the modelconverges with each such iteration. It will be appreciated by thoseskilled in the art that the model need not reach perfect accuracy toyield useful results. The combination of the iterative approach totraining, the use of teacher-student optimization to create a mergedmodel, and the recognition that perfect accuracy is not a requisite toachieving high quality results, means that the operator is required toreview far fewer than the total number of images in the image dataset200/205 to be able to be able to create custom models without the needfor extensive training in labeling or other tasks that have historicallybeen associated with deep learning detectors.

Once the model has been trained sufficiently, such that the merged model155 (FIGS. 1, 2 ) can respond well to a query addressing the new object1115, results of running the merged model against production data 165will yield for review by the operator images that are responsive to sucha query. The result of such an analysis for still images and videosnippets from an exemplary embodiment can be seen in FIGS. 15A and 15B,respectively. In FIG. 15A, still frame images determined to beresponsive to the query are shown in a queue at the left, where imagesdetermined to match the query with high confidence are shown at the topand indicated at 1510, images assigned medium confidence are located inthe middle and indicated at 1515, and images with low confidence butstill above a minimum confidence threshold are at the lower end of thequeue and indicated at 1520. The confidence values 1525 associated witheach image are shown to the right of the images, e.g., 96% for 1510, 67%and 61% for 1515, and 58% and 56% for 1520. Labels indicating thegeneral level of confidence, e.g., high, medium, low, can be provided atthe left of the queue, and color coded to permit rapid identification.While only three levels of confidence are shown in FIG. 15A, it will beappreciated that this is only exemplary and the number of levels isdiscretionary, including not having any levels at all and instead justindicating the confidence value for each image as shown at 1525. In anembodiment, images assigned a confidence value of at least 95% areassigned high confidence, images between 60% and 95% medium confidenceand, below that, low confidence. A selected image, in this example theupper one of images 1515, is shown in greater detail in the centerportion of FIG. 15A where it can be reviewed in detail by an operator.An optional timestamp 1530 can indicate when an image was taken,selected, or any other time-related characteristic and can serve as asorting criteria, 1535. Across the bottom of the screen can be displayeda row of thumbnails 1540 or similar reduced-size images representativeof each image that the system deemed responsive to the analysis. Each ofthese thumbnails can also be selected for review and disposition by anoperator.

FIG. 15B provides an exemplary embodiment of a UI screen 1545 fordisplaying video snippets that result from the analysis described above.The snippets responsive to the analysis are shown at the left, indicatedgenerally at 1550, with a selected snippet displayed in larger form 1555at the center of the screen. In the illustrated embodiment, the lengthof each snippet is indicated alongside the snippet, and the confidencevalue associated with the snippet is also displayed. In someembodiments, the snippets are displayed in order of confidence level,usually in decreasing order but either or another suitable ordering canbe implemented depending upon the context and the selected settings,accessed via settings icon 1560. The number of displayed snippets 1550can vary by implementation. For the snippet selected for review anddisplay at the center of the screen 1555, a timeline 1565 displays whenduring the snippet the object of interest was detected. Any of thedisplayed snippets 1555 can be selected by clicking on therepresentative image shown, and additional blocks or pages of snippetscan be selected by clicking on numbered squares shown at 1570. In anembodiment, the snippets are selected from one or more datasources,where the one or more datasources being queried is indicated at 1575.Because a search over a large corpus of video data can return a large,unwieldy number of hits, paginating the results of a search can providehelpful organization of those results. As just one example, a page ofsnippets can represent fifty results or other suitable number, or thenumber of results can be permitted to vary according to similarity ofconfidence percentages, duration or other desired criteria.

To increase or decrease the number of detections, the confidencethreshold can be adjusted to any desired level, for example by slider1580, shown in FIG. 15B as being at 20% although the confidence valuecan be set higher or lower depending upon context, operator preference,or other suitable criteria. The context of the display can be varied byclicking on “eye” icon 1585, and can switch among several types ofselections of the data to be displayed. Likewise, default confidencevalues can, in some embodiments, vary depending upon the criteria bywhich the data is selected for display. For example, in someembodiments, the confidence adjustment slider 1580 will appear bydefault when the “eye” icon is clicked to select an “Inspect” mode, butmay not appear by default an “Analysis Results” mode, and may appear in“Live Monitoring Alerts” mode, with each of those defaults adjustable byuser preference through the settings available at icon 1560.

The display of confidence percentages can also vary depending upon theselections of the data to be displayed to the operator. For example, inan embodiment of the Analysis Results display, confidence percentagesare hidden by default in the video player, and by default also hiddenfor objects displayed in the larger view shown at 1555. At the sametime, by default all detections exceeding a default low confidencethreshold, for example one percent, may be returned as search results,optionally arranged by confidence percentage. In contrast, the defaultsfor Live Monitoring Alerts may be, for example, to return all detectionsabove a default threshold of 20% confidence, with confidence percentagesalways visible. As noted above, the default values can be adjusted viathe settings accessible at icon 1560.

In an embodiment, “inspect” mode reveals to the operator all detectionsof any searched object or objects above a default confidence level, forexample 20%, with the identities of the searched objects visible at1590. Optionally, the user can be permitted to select which of theobjects shown at 1590 are revealed in inspect mode, surrounded by theirrespective bounding boxes. Again, the confidence threshold can beadjusted in at least some embodiments. Alternatively, inspect mode canalso be configured to reveal all objects detected by the system, whetheror not a given object is part of the analysis results, or can beconfigured to allow the operator to incrementally add types or classesof objects that the system will reveal in inspect mode. Inspect mode canthus be used by an operator to reveal associations between or amongdetected objects, where the types of detections to be revealed varieswith each iteration of a search. Inspect mode can also be use forverification step, to ensure that they system is successfully detectingall objects in a frame or a video sequence regardless whether includedin a given search. In any of the modes a given scene can be captured byclicking on “capture scene”, shown at 1595.

Having fully described a preferred embodiment of the invention andvarious alternatives, those skilled in the art will recognize, given theteachings herein, that numerous alternatives and equivalents exist whichdo not depart from the invention. It is therefore intended that theinvention not be limited by the foregoing description, but only by theappended claims.

We claim:
 1. A method for developing in one or more processors a mergeddeep learning model for classification and detection of one or morepreviously specified objects and at least one newly specified objectcomprising providing in one or more processors and associated storage asystem production model comprising a first deep learning model capableof detecting and classifying one or more previously specified objectsidentified through the use of at least some anchor bounding boxes,providing in one or more processors and associated storage an iteratedmodel comprising a second deep learning model capable, followingtraining, of detecting and classifying at least one newly specifiedobject, providing a system production training dataset representative ofthe previously specified objects to the system production model and theiterated model, providing a second training dataset representative of anewly specified object identified through the use of at least somebounding boxes to the system production model and the iterated model,processing, in both the system production model and the iterated model,at least the system production dataset and the second training datasetand generating a system training output and an iterated training output,respectively, optimizing the training output from the processing step byapplying classification and regression algorithms to the system trainingoutput and the iterated training output to generate an optimizedtraining output, supplying the optimized training output as the mergeddeep learning model configured to classify and detect at least some ofthe one or more previously specified objects and at least one of thenewly specified objects.
 2. The method of claim 1 wherein new unlabeleddata is supplied to the system production model, the iterated model, andthe optimizing step and the processing step includes processing the newunlabeled data.
 3. The method of claim 1 wherein at least one of thesystem production model and the iterated model is a single shot multiboxdetector.
 4. The method of claim 1 wherein classification comprisesdetermining the probability distribution of the presence of any of theobjects of interest, or the background at an anchor box.
 5. The methodof claim 1 wherein classification is modeled as a softmax function tooutput confidence of a foreground class or a background class.
 6. Themethod of claim 1 wherein regression is modeled as a non-linearmultivariate regression function.
 7. The method of claim 6 wherein themultivariate regression function outputs a four-dimensional vectorrepresenting center coordinates, width and height of the bounding boxenclosing the object in the image.
 8. The method of claim 1 wherein thesecond training dataset is only partly labeled.
 9. The method of claim 1wherein the system production model is interoperable with the iteratedmodel.
 10. A method for developing in one or more processors a studentdeep learning model for classification and detection of one or morepreviously specified objects and at least one newly specified object,comprising the steps of providing in one or more processors andassociated storage a first teacher model comprising a first deeplearning model capable of detecting and classifying at least onepreviously specified object, providing in one or more processors andassociated storage a second teacher model comprising a second deeplearning model configured for being trained to detect and classify atleast one newly specified object, providing to the first teacher modeland the second teacher model a first training dataset representative ofthe previously specified objects, providing to the first teacher modeland the second teacher model a second training dataset representative ofa newly specified object identified through the use of at least somebounding boxes, processing, in both the first teacher model and thesecond teacher model, at least the first training dataset and the secondtraining dataset and generating a first training output and a secondtraining output, respectively, optimizing the first and second trainingoutputs to generate an optimized training output from the processingstep by applying classification algorithms for determining theprobability distribution, at an anchor box, of the presence of either ofany of the objects of interest or the background and applying regressionalgorithms for determining the bounding box of an object that isdetected at the anchor box. supplying the optimized training output asthe student deep learning model configured to classify and detect atleast one of the one or more previously specified objects and at leastone of the newly specified objects.
 11. The method of claim 10 whereinat least the second training dataset comprises in part video snippets.12. The method of claim 10 wherein at least the second training datasetcomprises in part synthetic data.
 13. The method of claim 10 wherein thefirst teacher model and the second teacher model are interoperable. 14.The method of claim 13 wherein at least one of the first teacher modeland the second teacher model is a single shot multibox detector.
 15. Themethod of claim 10 wherein the second training output is provided to anoperator for correction and the corrected output is processed in asecond iteration of the processing step.
 16. The method of claim 10comprising the further step of providing a validation dataset to thefirst teacher model and the second teacher model.
 17. The method ofclaim 10 wherein at least some images are provided to an operator as theresult of an uncertainty calculation for distinguishing an object frombackground.
 18. The method of claim 17 wherein the uncertaintycalculation is based in part on a variable threshold.
 19. The method ofclaim 10 wherein a grid of anchor boxes is distributed uniformlythroughout an image.
 20. A method for developing in one or moreprocessors a merged deep learning model for classification and detectionof one more previously specified objects and at least one newlyspecified object, comprising the steps of providing in one or moreprocessors and associated storage a plurality of teacher models eachcomprising a deep learning model capable of detecting and classifying atleast one previously specified object and at least one newly specifiedobject, providing to each teacher model a plurality of first trainingdatasets, some of which are representative of at least some of the oneor more previously specified objects, providing to each teacher model atleast one new training dataset representative of the at least one newlyspecified object identified through the use of at least some boundingboxes, providing to each teacher model new unlabeled data, processing,in each of the teacher models, each of the plurality of trainingdatasets and the new unlabeled data and generating a training outputfrom each of the plurality of teacher models, optimizing the trainingoutput from each of the plurality of teacher models to generate anoptimized training output by applying classification algorithms andregression algorithms to each of the training outputs, supplying theoptimized training output as the merged deep learning model configuredto detect and classify one or more of the previously specified objectsand at least one newly specified object.
 21. The method of claim 20wherein each of the plurality of teacher models is interoperable withthe remainder of the plurality of teacher models.
 22. The method ofclaim 20 wherein at least some of the teacher models are selected from agroup comprising a single shot multibox detector and a low shot learningdetector.
 23. The method of claim 20 wherein the classificationalgorithms determine the probability distribution, at an anchor box, ofthe presence of either of any of the objects of interest or thebackground, and the regression algorithms determine the bounding box ofan object that is detected at the anchor box.
 24. The method of claim 20wherein generating the training output is fine tuned by propagating aloss function gradient.
 25. The method of claim 20 wherein at least onenew training dataset comprises in part synthetic data.
 26. A method fordeveloping in one or more processors a student deep learning model forclassification and detection of one more previously specified objectsand at least one newly specified object, comprising the steps ofproviding in one or more processors and associated storage at least oneof a first teacher model comprising one of either a single shot multiboxdetector or a low shot learning detector configured to classify anddetect at least one previously specified object, providing in one ormore processors and associated storage at least one of a second teachermodel comprising one of either a single shot multibox detector or a lowshot learning detector configured for being trained to classify anddetect at least one newly specified object, providing to each firstteacher model and each second teacher model a first training datasetrepresentative of the previously specified objects, providing to eachfirst teacher model and each second teacher model at least one newtraining dataset representative of a newly specified object, processing,in each first teacher model and each second teacher model, the firsttraining dataset and the at least one new training dataset andgenerating a first training output and at least one new training output,respectively, optimizing the first training output and new trainingoutputs to generate an optimized training output from the processingstep by applying classification algorithms and regression algorithms,supplying the optimized training output as the student deep learningmodel configured to classify and detect at least some of the one or morepreviously specified objects and at least one of the newly specifiedobjects.
 27. The method of claim 26 wherein at least one of the firstteacher model is interoperable with at least one of the second teachermodel.
 28. The method of claim 26 further comprising the step of testingthe merged model by processing production data to generate a productionoutput and feeding the production output to an operator.
 29. The methodof claim 26 wherein only part of the at least one new training datasetis labeled.
 30. The method of claim 26 further comprising the step ofiteratively improving the student deep learning model by testing foruncertainty as to whether an anchor box includes an object and, for aplurality of anchor boxes, sorting according to uncertainty values.